indicated they would recommend the unit to others.
In addition, the majority of the high school and university students reported that it increased their interest in AI. These results are particularly notable given
the wide age range and cultural backgrounds of participants, and suggests that our approach was broadly accessible and engaging.

The benefits of problem-to-project scaffolding in
our work included having a unifying theme, enabling
both learners and instructors to tie concepts and tasks
back to a central idea. A related benefit was that the
theme afforded the ability to enhance relevance, especially for audiences outside of the classroom, highlighting the value of our approach for teaching concepts in AI to learners representing a broad swath of
age, gender, and cultural backgrounds. Additionally,
learners showed relatively high levels of engagement,
as evidenced by survey findings and qualitative feedback. Being able to meet students where they were at,
and serve different learning styles using a variety of
activities, was another benefit of the broader scaffolding approach. Drawbacks of our scaffolding framework included somewhat of a narrow focus: by beginning with a particular security problem, a
circumscribed range of AI topics fit with the unit. This
also led to development of instructional materials
specific to the problem.

These findings suggest that our approach may
potentially be applied successfully to additional audiences. For instance, our approach can help address
educators’ needs, as a recent study found that many
are seeking more cutting-edge classroom materials
(Wollowski et al. 2015). A possible additional target
group is security organizations that could benefit
from security applications based on AI and game theory. Enhancing decision makers’ and field officers’
understanding of the theory on which these applications are based could foster the adoption of emerging AI-based decision aids.

Limitations and Future Directions

Our findings should be viewed in light of several
limitations. First, it is important to note that
although the wildlife security problems used in our
program were based on real-world data and input
from security experts working in the field on such
problems, they represent an abstracted version of
the problems they aim to address. In addition, it is
unclear how our approach would generalize to
teaching AI topics beyond game theory and security games. In the future, we plan to adapt our activities to focus on other AI topics. We also plan to
incorporate new activities; for instance, an activity
for learners to analyze defender strategies in depth,
bringing to light subtle human biases that may
affect initial strategies, thereby highlighting benefits of AI agents (compared to humans) in decision
making.

Acknowledgements

This work was supported by MURI Grant W911NF-
11-1-03 and World Wildlife Fund PAWS Anti-Poach-ing Grant. The authors would like to thank Job
Charles, Rois Mahmud, and Citra Ayu Wardani for
their integral roles in planning and implementing
the workshop. We also thank Sarine Aratoon, Jori
Barash, and Elliott Wezerek for their help with
preparing the data.